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Dynamic SBI Round-free Sequential Simulation-Based Inference with Adaptive Datasets

H Lyu, J Alvey, NA Montel, M Pieroni… - arXiv preprint arXiv …, 2025 - arxiv.org
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Simulation-based inference (SBI) is emerging as a new statistical paradigm for addressing complex scientific inference problems. By leveraging the representational …

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@article{2510.13997v1,
Author        = {Huifang Lyu and James Alvey and Noemi Anau Montel and Mauro Pieroni and Christoph Weniger},
Title         = {Dynamic SBI: Round-free Sequential Simulation-Based Inference with
  Adaptive Datasets},
Eprint        = {2510.13997v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {Simulation-based inference (SBI) is emerging as a new statistical paradigm
for addressing complex scientific inference problems. By leveraging the
representational power of deep neural networks, SBI can extract the most
informative simulation features for the parameters of interest. Sequential SBI
methods extend this approach by iteratively steering the simulation process
towards the most relevant regions of parameter space. This is typically
implemented through an algorithmic structure, in which simulation and network
training alternate over multiple rounds. This strategy is particularly well
suited for high-precision inference in high-dimensional settings, which are
commonplace in physics applications with growing data volumes and increasing
model fidelity. Here, we introduce dynamic SBI, which implements the core ideas
of sequential methods in a round-free, asynchronous, and highly parallelisable
manner. At its core is an adaptive dataset that is iteratively transformed
during inference to resemble the target observation. Simulation and training
proceed in parallel: trained networks are used both to filter out simulations
incompatible with the data and to propose new, more promising ones. Compared to
round-based sequential methods, this asynchronous structure can significantly
reduce simulation costs and training overhead. We demonstrate that dynamic SBI
achieves significant improvements in simulation and training efficiency while
maintaining inference performance. We further validate our framework on two
challenging astrophysical inference tasks: characterising the stochastic
gravitational wave background and analysing strong gravitational lensing
systems. Overall, this work presents a flexible and efficient new paradigm for
sequential SBI.},
Year          = {2025},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2510.13997v1},
File          = {2510.13997v1.pdf}
}

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